Large Scale Grid Integration of Renewable Energy Sources

Buy e-book PDF

As renewable energy sources have reached grid parity in many countries, the key to further growth of the share of renewables in the power mix is their integration with the power system. This requires a number of technical developments, for example in power electronics, to meet the need for increased flexibility and rapid dispatch. This book explores the new approaches to meet these challenges, such as increasing interconnection capacity among geographical areas, hybridization of different distributed energy resources and building up demand response capabilities. Topics covered include power grid as part of a 100% renewable energy system; international requirements for large integration of renewable energy sources; nowcasting and short-term wind forecasting for wind energy management; solutions and active measures for wind power integration; grid Integration of large-scale PV plants; extensive use of renewable energy resources: needs, conditions and enabling technologies; DC distribution systems and microgrids; distributed energy resources integration and demand response; stochastic demand modelling; and distributed micro-storage systems at residential level in smart communities with high penetration of photovoltaic generation. This book is essential reading for researchers involved with clean energy generation and power systems.

The large-scale integration of VRE into power grids requires substantial transformations to increase the power system flexibility: (1) to allow bidirectional electricity flow, which is aimed to ensure grid PQR when including DER; (2) to establish DR mechanisms aimed at reducing peak-loads in order to deal with increased variability; (3) to expand grid interconnection at the regional and international level aimed at increasing balancing capabilities and stability; (4) include technologies and procedures to ensure adequate stability and control (e.g., frequency, voltage, power balance) in the presence of VRE and (5) to exploit the ESS potential to store electricity surplus from VRE. Smart grids have the potential to benefit the whole value chain so that system operators can get the required power security and quality of supply.

Most European countries have concerns about the integration of large amounts of renewable energy sources (RES) into electric power systems, and this is currently a topic of growing interest. In January 2008, the European Commission published the 2020 package, which proposes committing the European Union to a 20% reduction in greenhouse gas emissions, to achieve a target of deriving 20% of the European Union's final energy consumption from renewable sources, and to achieve 20% improvement in energy efficiency both by the year 2020 [1]. Member states have different individual goals to meet these overall objectives, and they each need to provide a detailed roadmap describing how they will meet these legally binding targets [2]. At this time, RES are an indispensable part of the global energy mix, which has been partially motivated by the continuous increases in hydropower as well as the rapid expansion of wind and solar photovoltaic (PV). The International Energy Agency's 2012 edition of the World Energy Outlook stated that the rapid increases in RES integration are underpinned by falling technology costs as well as rising fossilfuel prices and carbon pricing, but RES integration is also encouraged by continued subsidies: from $88 billion globally in 2011 (compared to $523 billion in fossil-fuel subsidies in 2012 [3], with a share of $131 billion for electricity generation) to an estimated $240 billion in 2035 [4]. According to [3], in 2015 RES accounted for 22% of electricity generation, which was approximately the same level as gas and about one-half the level of coal.

The blooming an integration of these data sources would impulse and consolidate the use of energy nowcasting. As said, the development of nowcasting techniques based on big data or crowdsourcing, could benefit wind energy integration. But they also could represent an important tool for emerging configurations of power systems, as micro-grids and nano-grids. These grids range in a scale between one building to a small community of neighbours. The integration of wind energy in this context demands low-cost and easy-to-implement wind power forecasting products, and the developments in nowcasting could provide satisfactory solutions.

The limited supply of fossil fuels, increasing demand for energy, and concern about the human environmental impact cannot ensure sustainability under the circumstances of existing fossil fuelled power plants. Today, wind technology is seen as the most prospective solution of alternative and sustainable sources of energy. However, its large-scale integration requires fundamental changes in the planning and operation of power systems as well as the management and attitudes towards different market and power system participants. Only synergy between consumers, producers and system operators will overcome the challenges faced with large-scale wind integration.

This chapter will study the dynamics of irradiance and the power delivered to the grid by PV plants in time frames of less than 10 min, paying particular attention to the influence of PV plant size and geographic dispersion on magnitude and duration. We are now going to present some general models which make it possible to simulate the power fluctuations of either a power plant or a group of power plants, based solely on irradiance measurements. Finally, we shall study the control strategies required to compensate these fluctuations, based on storage systems installed in the PV plants, analysing the energy and power requirements to compensate these variations.

This chapter has justified the choice of the PTP standard for coordinating all of the equipment located in an installation as the most appropriate way of extending it to other installations in a wide geographical area. It is possible to reduce the amount of equipment equipped with GPS, but guaranteeing the synchronism with a quality equivalent to 1 ms. An intelligent network must evolve dynamically according to demand. In this context, it is essential to maintain a control of the stability of the network in a greater number of control points. The current technology has lowered the costs of high-performance electronic systems. As discussed in this chapter, PMUs or synchrophasors are an essential piece, and their integration into the IEC 61850 standard allows a local and centralised management system to be defined that cooperates with other systems located in the network, such as SPQAs and intelligent inverters.

A qualitative overview of different hardware topologies and control systems for DC MGs has been presented in this chapter. Some challenges and design considerations of DC protections systems have also been discussed. Finally, applications of DC MGs in emerging smart grid applications have been summarized. Due to its attractive characteristics in terms of compliance with modern generation, storage and electronic load technologies, high reliability and current carrying capacity, as well as simple control, DC systems are already an indispensable part of power systems. Moreover, the existing challenges such as protection issues will be effectively resolved in the near future due to fast progress of semiconductor technology which is a key enabler cheap and reliable future DC solid-state protection systems. Therefore, it is the view of the author that more and more DC systems will appear in different industries and gradually lead to new ways of rethinking of the future power distribution philosophies, especially with the emergence of SSTs. Research in DC systems, especially in the power electronics-based technologies will be highly attractive in the future.

One of the main problems with it comes to the integration of distributed energy resources is the estimation and prediction of the energetic demand that those energy sources must be able to supply [1]. This is especially difficult nowadays due to the upward trend observed in all the projections for the energy consumption for major energy end-use sectors (residential, commercial, industrial, and transportation) [2]. In this context, stochastic modelling techniques have been presented as the most suitable ones as they are able to create the diversity needed to take into account the behaviour of the residents while keeping the general observed statistical trends [7]. These methods are based on the individual modelling of each appliance and the consumer behaviour patterns so not can only high-resolution profiles be generated, but also the end-use of the energy can be specifically determined. That is not even possible nowadays with the new advanced metering infrastructure, unless non-intrusive load monitoring programmes are applied, which also makes these models a great tool for the assessment of energy policies and the impact of including new appliances at home [8]. In this chapter, the usage and application of stochastic demand models will be addressed, with a focus on the prediction and estimation of the energy consumption, the assessment of different demand response policies and the integration of distributed energy resources at small scale. For this aim, first, a short overview of the main modelling techniques employed in the residential sector will be given to the reader in order to contextualise this type of models. Subsequently, the methodology used in these models will be exposed, showing the different blocks that usually built up the estimation process. Finally, the usability for assessing energy policies and integrating distributed energy resources will be discussed.

In this line, this chapter aims to provide an analysis of distributed microstorage energy systems at the residential level to contribute to smart grid goals. The chapter is organized in five sections as follows: first an overview of micro-storage technologies, including the state of the art, is presented. It will also be shown the advantages when using different ESS unified in a hybrid energy storage system (HESS), achieving best parameters for specific applications than the ESS operating individually. The second section is devoted to the suitable topologies for the bi-directional electronic converter responsible for the flux between the micro-storage energy system located at home and the grid by means of the smart community energy management system (SCEMS). Afterwards, in the third section control strategies for the ESMS of the microstorage device are proposed, following different compensation objectives from the set-points provided by the SCEMS. Furthermore, these strategies will ensure at all times safety and will improve the efficiency and lifetime of the ESS. In the fourth section, the attention is paid to the power interfaces for the integration of ESS into the grid to adapt the requirements of distributed microstorage system in a smart community with high penetration of photovoltaic systems.